Abstract [en]

As wind farms continue to take up more land throughout Northern Europe, developers are looking to sparsely populated areas, particularly in northern Fennoscandia, which hosts strong winds but also mixed and patchy forests over complex terrain. The complexity makes wind resource assessments difficult, raising uncertainty and therefore cost. Computational fluid dynamics (CFD) has the potential to increase the accuracy and reliability of wind models, but the most common form of commercial CFD modeling, Reynolds averaged Navier-Stokes (RANS), makes limiting assumptions about the effect of the forest on the wind. The wind resource assessment and energy estimation tool WindSim® , developed by WindSim AS, utilizes a porous medium model of a homogeneous forest with the influence of the forest on the airflow as a drag force term in the momentum equations. This method has provided reliable wind speed results but has been less reliable in estimating turbulence characteristics. The measure we evaluate in this study is turbulence intensity (TI). In this investigation, we make two types of modifications to the model and evaluate their impact on the TI estimates by using a benchmark data set collected by Meroney [1]. The first method is a variable profile of leaf area index (LAI) to represent the physical shape of the forest more accurately, and the second is a series of modifications to the closure coefficients in the turbulence transport equations. These modifications focus on the work of Lopes et al. [2], who used a large eddy simulation (LES) model to show that the turbulence production terms originally proposed by Green [3], expanded upon by Sanz [4], and widely used in the industry are unnecessary. Our investigations found that the implementation of a variable LAI profile has a small but non-negligible effect and that the elimination of the production terms from the turbulence transport equations does lead to a significant reduction in TI immediately above the forest. Both methods have minor effects on wind speed estimates, but the modification of closure coefficients has a much more significant impact on the TI. The coefficients proposed by Lopes et al. [2] drastically reduce TI estimates, but the model is still unable to reflect the Meroney data throughout the forest. Continued modification to new closure coefficients in combination with a variable forest LAI and other modifications such as a limited length scale may lead to significant improvement in TI estimates in future models, but these modifications must be compared against real-world data to ensure their applicability.